Development of a breast reconstruction satisfaction questionnaire (BRECON): Dimensionality and clinical importance of breast symptoms, donor site issues, patient expectations, and relationships
Bibliographic record
Abstract
OBJECTIVES: To develop a valid, reliable and responsive, self-administered questionnaire to assess women's satisfaction with breast reconstruction. METHODS: Item generation: Three sources for item inventory were utilized: focus groups, expert panel, and literature review.Item reduction: Item impact scores were derived from patients and experts each ranking the importance and frequency of each item. Correlation between patient and expert scores was calculated. The highest impact questions were maintained. RESULTS: Four focus groups comprising 20 women generated 515 items, 10 experts developed 171 items, and literature review produced 227 items. These 913 potential items were reduced to 183 by combining redundancy. The 183 items underwent formal reduction by assessing importance and frequency of each item. Thirty-two of 40 reconstructed women and 19 of 19 experts responded to the mail-out. Seventy-seven items of the women's top 100 also made the experts' top 100 list. Intraclass correlation between patients and experts was 0.71 [0.62 0.77], indicating "good" but not "excellent" agreement, reinforcing the importance of patient involvement in questionnaire development. Women rated abdominal donor site issues higher than experts, and experts rated breast softness and symmetry higher than women. CONCLUSIONS: A 100-item pilot questionnaire for breast reconstruction satisfaction was developed for psychometric testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".